Calculate the dot product of two single-precision floating-point vectors with extended accumulation.
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> Calculate the dot product of two single-precision floating-point vectors with extended accumulation.
The [dot product][dot-product] (or scalar product) is defined as
``bash`
npm install @stdlib/blas-base-sdsdot
`javascript`
var sdsdot = require( '@stdlib/blas-base-sdsdot' );
#### sdsdot( N, scalar, x, strideX, y, strideY )
Calculates the dot product of vectors x and y with extended accumulation.
`javascript
var Float32Array = require( '@stdlib/array-float32' );
var x = new Float32Array( [ 4.0, 2.0, -3.0, 5.0, -1.0 ] );
var y = new Float32Array( [ 2.0, 6.0, -1.0, -4.0, 8.0 ] );
var z = sdsdot( x.length, 0.0, x, 1, y, 1 );
// returns -5.0
`
The function has the following parameters:
- N: number of indexed elements.
- scalar: scalar constant added to the dot product.
- x: input [Float32Array][@stdlib/array/float32].x
- strideX: index increment for .Float32Array
- y: input [][@stdlib/array/float32].y
- strideY: index increment for .
The N and stride parameters determine which elements in the strided arrays are accessed at runtime. For example, to calculate the dot product of every other value in x and the first N elements of y in reverse order,
`javascript
var Float32Array = require( '@stdlib/array-float32' );
var x = new Float32Array( [ 1.0, 2.0, 3.0, 4.0, 5.0, 6.0 ] );
var y = new Float32Array( [ 1.0, 1.0, 1.0, 1.0, 1.0, 1.0 ] );
var z = sdsdot( 3, 0.0, x, 2, y, -1 );
// returns 9.0
`
Note that indexing is relative to the first index. To introduce an offset, use [typed array][mdn-typed-array] views.
`javascript
var Float32Array = require( '@stdlib/array-float32' );
// Initial arrays...
var x0 = new Float32Array( [ 1.0, 2.0, 3.0, 4.0, 5.0, 6.0 ] );
var y0 = new Float32Array( [ 7.0, 8.0, 9.0, 10.0, 11.0, 12.0 ] );
// Create offset views...
var x1 = new Float32Array( x0.buffer, x0.BYTES_PER_ELEMENT*1 ); // start at 2nd element
var y1 = new Float32Array( y0.buffer, y0.BYTES_PER_ELEMENT*3 ); // start at 4th element
var z = sdsdot( 3, 0.0, x1, -2, y1, 1 );
// returns 128.0
`
#### sdsdot.ndarray( N, x, strideX, offsetX, y, strideY, offsetY )
Calculates the dot product of vectors x and y with extended accumulation and using alternative indexing semantics.
`javascript
var Float32Array = require( '@stdlib/array-float32' );
var x = new Float32Array( [ 4.0, 2.0, -3.0, 5.0, -1.0 ] );
var y = new Float32Array( [ 2.0, 6.0, -1.0, -4.0, 8.0 ] );
var z = sdsdot.ndarray( x.length, 0.0, x, 1, 0, y, 1, 0 );
// returns -5.0
`
The function has the following additional parameters:
- offsetX: starting index for x.y
- offsetY: starting index for .
While [typed array][mdn-typed-array] views mandate a view offset based on the underlying buffer, the offset parameters support indexing semantics based on starting indices. For example, to calculate the dot product of every other value in x starting from the second value with the last 3 elements in y in reverse order
`javascript
var Float32Array = require( '@stdlib/array-float32' );
var x = new Float32Array( [ 1.0, 2.0, 3.0, 4.0, 5.0, 6.0 ] );
var y = new Float32Array( [ 7.0, 8.0, 9.0, 10.0, 11.0, 12.0 ] );
var z = sdsdot.ndarray( 3, 0.0, x, 2, 1, y, -1, y.length-1 );
// returns 128.0
`
- If N <= 0, both functions return scalar.sdsdot()
- corresponds to the [BLAS][blas] level 1 function [sdsdot][sdsdot].
`javascript
var discreteUniform = require( '@stdlib/random-array-discrete-uniform' );
var sdsdot = require( '@stdlib/blas-base-sdsdot' );
var opts = {
'dtype': 'float32'
};
var x = discreteUniform( 10, 0, 100, opts );
console.log( x );
var y = discreteUniform( x.length, 0, 10, opts );
console.log( y );
var out = sdsdot( x.length, 0.0, x, 1, y, -1 );
console.log( out );
`
*
`c`
#include "stdlib/blas/base/sdsdot.h"
#### c_sdsdot( N, scalar, \X, strideX, \Y, strideY )
Calculates the dot product of vectors x and y with extended accumulation.
`c
const float x[] = { 1.0f, -2.0f, 3.0f, -4.0f, 5.0f, -6.0f, 7.0f, -8.0f };
const float y[] = { 1.0f, -2.0f, 3.0f, -4.0f, 5.0f, -6.0f, 7.0f, -8.0f };
float v = c_sdsdot( 5, 0.0f, x, 1, y, -1 );
// returns -120.0f
`
The function accepts the following arguments:
- N: [in] CBLAS_INT number of indexed elements.[in] float
- scalar: scalar constant to add to dot product.[in] float*
- X: first input array.[in] CBLAS_INT
- strideX: index increment for X.[in] float*
- Y: second input array.[in] CBLAS_INT
- strideY: index increment for Y.
`c`
float c_sdsdot( const CBLAS_INT N, const float scalar, const float X, const CBLAS_INT strideX, const float Y, const CBLAS_INT strideY );
#### c_sdsdot_ndarray( N, scalar, \X, strideX, offsetX, \Y, strideY, offsetY )
Calculates the dot product of vectors x and y with extended accumulation using alternative indexing semantics.
`c
const float x[] = { 1.0f, -2.0f, 3.0f, -4.0f, 5.0f, -6.0f, 7.0f, -8.0f };
const float y[] = { 1.0f, -2.0f, 3.0f, -4.0f, 5.0f, -6.0f, 7.0f, -8.0f };
float v = c_sdsdot_ndarray( 5, 0.0f, x, 1, 0, y, -1, 7 );
// returns -80.0f
`
The function accepts the following arguments:
- N: [in] CBLAS_INT number of indexed elements.[in] float
- scalar: scalar constant to add to dot product.[in] float*
- X: first input array.[in] CBLAS_INT
- strideX: index increment for X.[in] CBLAS_INT
- offsetX: starting index for X.[in] float*
- Y: second input array.[in] CBLAS_INT
- strideY: index increment for Y.[in] CBLAS_INT
- offsetY: starting index for Y.
`c`
float c_sdsdot_ndarray( const CBLAS_INT N, const float scalar, const float X, const CBLAS_INT strideX, const CBLAS_INT offsetX, const float Y, const CBLAS_INT strideY, const CBLAS_INT offsetY );
`c
#include "stdlib/blas/base/sdsdot.h"
#include
int main( void ) {
// Create strided arrays:
const float x[] = { 1.0f, -2.0f, 3.0f, -4.0f, 5.0f, -6.0f, 7.0f, -8.0f };
const float y[] = { 1.0f, -2.0f, 3.0f, -4.0f, 5.0f, -6.0f, 7.0f, -8.0f };
// Specify the number of indexed elements:
const int N = 8;
// Specify strides:
const int strideX = 1;
const int strideY = -1;
// Compute the dot product:
float d = c_sdsdot( N, 0.0f, x, strideX, y, strideY );
// Print the result:
printf( "dot product: %f\n", d );
// Compute the dot product:
d = c_sdsdot_ndarray( N, 0.0f, x, strideX, 0, y, strideY, 7 );
// Print the result:
printf( "dot product: %f\n", d );
}
`
*
- Lawson, Charles L., Richard J. Hanson, Fred T. Krogh, and David Ronald Kincaid. 1979. "Algorithm 539: Basic Linear Algebra Subprograms for Fortran Usage \[F1]." _ACM Transactions on Mathematical Software_ 5 (3). New York, NY, USA: Association for Computing Machinery: 324–25. doi:[10.1145/355841.355848][@lawson:1979a].
*
- [@stdlib/blas-base/ddot][@stdlib/blas/base/ddot]: calculate the dot product of two double-precision floating-point vectors.
- [@stdlib/blas-base/dsdot][@stdlib/blas/base/dsdot]: calculate the dot product with extended accumulation and result of two single-precision floating-point vectors.
- [@stdlib/blas-base/sdot][@stdlib/blas/base/sdot]: calculate the dot product of two single-precision floating-point vectors.
*
This package is part of [stdlib][stdlib], a standard library for JavaScript and Node.js, with an emphasis on numerical and scientific computing. The library provides a collection of robust, high performance libraries for mathematics, statistics, streams, utilities, and more.
For more information on the project, filing bug reports and feature requests, and guidance on how to develop [stdlib][stdlib], see the main project [repository][stdlib].
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---
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Copyright © 2016-2026. The Stdlib [Authors][stdlib-authors].
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[dot-product]: https://en.wikipedia.org/wiki/Dot_product
[blas]: http://www.netlib.org/blas
[sdsdot]: http://www.netlib.org/lapack/explore-html/df/d28/group__single__blas__level1.html
[@stdlib/array/float32]: https://www.npmjs.com/package/@stdlib/array-float32
[mdn-typed-array]: https://developer.mozilla.org/en-US/docs/Web/JavaScript/Reference/Global_Objects/TypedArray
[@lawson:1979a]: https://doi.org/10.1145/355841.355848
[@stdlib/blas/base/ddot]: https://www.npmjs.com/package/@stdlib/blas-base-ddot
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[@stdlib/blas/base/sdot]: https://www.npmjs.com/package/@stdlib/blas-base-sdot